The bioremediation of soils polluted with hydrocarbons demonstrated to be a simple and cheap technique, even if it needs a long time. The current paper shows the application of statistical analysis, based on two factors involved in the biological process at several levels. We focus on the Design of Experiments (DOE) to determine the number and kind of experimental runs, whereas the use of the categorical factors has not been widely exploited up to now. This method is especially useful to analyze factors with levels constituted by categories and define the interaction effects. Particularly, we focused on the statistical analysis of (1) experimental runs carried out at laboratory scale (test M, in microcosm), on soil polluted with diesel oil, and (2) bench scale runs (test B, in biopile), on refinery oil sludge mixed with industrial or agricultural biodegradable wastes. Finally, the main purpose was to identify the factor’s significance in both the tests and their potential interactions, by applying the analysis of variance (ANOVA). The results demonstrate the robustness of the statistical method and its quality, especially when at least one of the factors cannot be defined with a numerical value.
Bioremediation of Hydrocarbon-Polluted Soil: Evaluation of Different Operative Parameters / CASTRO RODRIGUEZ, DAVID JAVIER; Gutiérrez Benítez, Omar; Casals Pérez, Enmanuel; Demichela, Micaela; Godio, Alberto; Chiampo, Fulvia. - In: APPLIED SCIENCES. - ISSN 2076-3417. - ELETTRONICO. - 12:4(2022), p. 2012. [10.3390/app12042012]
Bioremediation of Hydrocarbon-Polluted Soil: Evaluation of Different Operative Parameters
David Javier Castro Rodríguez;Micaela Demichela;Alberto Godio;Fulvia Chiampo
2022
Abstract
The bioremediation of soils polluted with hydrocarbons demonstrated to be a simple and cheap technique, even if it needs a long time. The current paper shows the application of statistical analysis, based on two factors involved in the biological process at several levels. We focus on the Design of Experiments (DOE) to determine the number and kind of experimental runs, whereas the use of the categorical factors has not been widely exploited up to now. This method is especially useful to analyze factors with levels constituted by categories and define the interaction effects. Particularly, we focused on the statistical analysis of (1) experimental runs carried out at laboratory scale (test M, in microcosm), on soil polluted with diesel oil, and (2) bench scale runs (test B, in biopile), on refinery oil sludge mixed with industrial or agricultural biodegradable wastes. Finally, the main purpose was to identify the factor’s significance in both the tests and their potential interactions, by applying the analysis of variance (ANOVA). The results demonstrate the robustness of the statistical method and its quality, especially when at least one of the factors cannot be defined with a numerical value.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2955524